Achieving k-Anonymity by clustering in attribute hierarchical structures
Paper
Paper/Presentation Title | Achieving k-Anonymity by clustering in attribute hierarchical structures |
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Presentation Type | Paper |
Authors | Li, Jiuyong (Author), Wong, Raymond Chi-Wing (Author), Fu, Ada Wai-Chee (Author) and Pei, Jian (Author) |
Editors | Tjoa, A. Min and Trujillo, Juan |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 4081, pp. 405-416 |
Number of Pages | 12 |
Year | 2006 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783540377368 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/11823728 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/11823728_39 |
Conference/Event | 8th International Conference on Data Warehousing and Knowledge Discovery |
Event Details | 8th International Conference on Data Warehousing and Knowledge Discovery Event Date 04 to end of 08 Sep 2006 Event Location Krakow, Poland |
Abstract | Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. A more general view of k-anonymity is clustering with a constraint of the minimum number of objects in every cluster. Most existing approaches to achieving k-anonymity by clustering are for numerical (or ordinal) attributes. In this paper, we study achieving k-anonymity by clustering in attribute hierarchical structures. We define generalisation distances between tuples to characterise distortions by generalisations and discuss the properties of the distances. We conclude that the generalisation distance is a metric distance. We propose an efficient clustering-based algorithm for k-anonymisation. We experimentally show that the proposed method is more scalable and causes significantly less distortions than an optimal global recoding k-anonymity method. |
Keywords | data mining; privacy preserving; k-anonymity |
ANZSRC Field of Research 2020 | 461305. Data structures and algorithms |
460908. Information systems organisation and management | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Department of Mathematics and Computing |
Chinese University of Hong Kong, China | |
Simon Fraser University, Canada |
https://research.usq.edu.au/item/9y0wz/achieving-k-anonymity-by-clustering-in-attribute-hierarchical-structures
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